Books like Bayesian parametric inference by Ashok K. Bansal

A practical guide to Bayesian statistical analysis that prioritizes computation, model checking, and data‑collection design, with examples spanning regression, hierarchical, robust, generalized linear, and mixture models.

Monte Carlo statistical methods, particularly those based on Markov chains, are now an essential component of the standard set of techniques used by statisticians. This new edition has been revised towards a coherent and flowing coverage of these simulation techniques, with incorporation of the most recent developments in the field. In particular, the introductory coverage of random variable generation has been totally revised, with many concepts being unified through a fundamental theorem of simulation. There are five completely new chapters that cover Monte Carlo control, reversible jump, slice sampling, sequential Monte Carlo, and perfect sampling. There is a more in-depth coverage of Gibbs sampling, which is now contained in three consecutive chapters. The development of Gibbs sampling starts with slice sampling and its connection with the fundamental theorem of simulation, and builds up to two-stage Gibbs sampling and its theoretical properties. A third chapter covers the multi-stage Gibbs sampler and its variety of applications. Lastly, chapters from the previous edition have been revised towards easier access, with the examples getting more detailed coverage. This textbook is intended for a second year graduate course, but will also be useful to someone who either wants to apply simulation techniques for the resolution of practical problems or wishes to grasp the fundamental principles behind those methods. The authors do not assume familiarity with Monte Carlo techniques (such as random variable generation), with computer programming, or with any Markov chain theory (the necessary concepts are developed in Chapter 6). A solutions manual, which covers approximately 40% of the problems, is available for instructors who require the book for a course. --back cover

Elementary Bayesian Statistics is a thorough and easily accessible introduction to the theory and practical application of Bayesian statistics. It presents methods to assist in the collection, summary and presentation of numerical data. Bayesian statistics are becoming an increasingly important and more frequently used method for analysing statistical data. The author defines concepts and methods with a variety of examples and uses a stage-by-stage approach to coach the reader through the applied examples. Also included are a wide range of problems to challenge the reader and the book makes extensive use of Minitab to apply computational techniques to statistical problems. Issues covered include probability, Bayes's Theorem and categorical states, frequency, the Bernoulli process and Poisson process, estimation, testing hypotheses and the normal process with known parameters and uncertain parameters. Elementary Bayesian Statistics will be an essential resource for students as a supplementary text in traditional statistics courses. It will also be welcomed by academics, researchers and econometricians wishing to know more about Bayesian statistics.